Literature Review:
Generally speaking, both temporal and spatial correlations exist within the time-variant request pattern of multimedia content. While spatial correlation reflects different users' preferences, depending on the geographical location and users' contextual information, the temporal correlation represents the variation of content popularity over time. In this context, several DNN models [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]} are introduced to capture the temporal and/or spatial features of user preferences in proactive caching schemes. For instance, Yu et al. [10]} used an auto-encoder model to predict users' preferences in the future by learning the latent representation of raw data in an unsupervised fashion. Auto-encoder models, however, suffer from training complexity. Tsai et al. [11]} used Convolutional Neural Network (CNN) for predicting users' interests based on sentence analysis. Ndikumana et al. [12]} introduced a DNN-based caching framework, compromising of Multi-Layer Perceptron (MLP) and CNN models, where contextual information such as age, emotion, and gender are utilized for making caching decisions. Although CNN-based proactive caching schemes have local spatial feature awareness, they are inefficient for extracting temporal features from the patterns of sequential requests. Furthermore, such models require multi-source input such as regional information, and contextual information of users to improve the cache performance. Therefore, they need an efficient data pre-processing model to extract this information.
